We adapt the two-step approach of [1]} to estimate a
low-rank GFPCA representation of the registered curves
\(Y_i \left( t \right) = Y_i \left( h^{-1}_i(t_i^*)\right)\) .
Following [2]} and the groundwork of [3]},
functional principal components (FPCs) are estimated using a marginal, semiparametric
method based on assuming a latent Gaussian process \(X_i(t)\)  so that
\(\begin{aligned}\mathsf {E}[Y_i(t)] &= \mu _i(t) = g[X_i(t)],\\X_i(t) &\approx \alpha (t) + \sum \limits _{k=1}^K c_{i,k} \cdot \psi _k(t),\end{aligned}\) 
